Applied Network Analysis


Graphs are a powerful data abstraction that allows for the representation and study of the complex relationships between elements of a wide variety of systems (e.g., individuals and their social relationships in a population). Stemming from mathematics, Graphs have become a popular framework for tackling problems in many domains. For instance, in social sciences, Social Network Analysis (SNA) has been used to study social influence, the spread of innovations, and the formation of human capital; in Physics and Computer Sciences, complex networks have been key to understanding the functioning of complex systems; in Ecology, graphs have helped draw a picture about the interdependence between species and ecosystems and in showing their fragility and susceptibility to collapse; and in Financial Systems, networks have shed light on the systemic risk posed by different actors in financial institutions.

But networks are not only a topic of academic and scientific interest. Networks are often used to visualize and understand interrelated structures in business. To optimize the storage and manipulation of unstructured data. It has been used to foster innovation, identify relevant individuals in populations, optimize teams and logistics infrastructures, and generate realistic what-if scenarios to support decision-making. 

Processing network data typically requires a combination of graph analytics, data science, and machine learning. In Applied Network Analysis, students will first be introduced to the fundamental concepts of the science of Network Science and then to analytical techniques and applications. Students will learn how to formulate network-related questions from data and to bring value from their answers.

  • The curricular unit in Applied Network Analysis consists of 14 weekly classes.
  • Each class lasts 120 minutes, which takes place between 20:30 and 22:30 on Tuesdays.
  • Classes are divided into Lectures (theoretical) and Labs (practical)
  • All materials (syllabus, slides, readings, documentation) are shared through the dedicated Moodle page of this curricular unit 

Caracterização geral





Professor responsável

Flávio Luís Portas Pinheiro


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Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês


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Método de ensino

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